| name | data-analysis |
| description | Analyze source data, tables, CSV/XLSX extracts, metrics, or experiment results with explicit data-authenticity checks. Use for profiling, cleaning assumptions, statistical summaries, segmentation, trend analysis, or "analyze this data"; do not use for spreadsheet file surgery, chart design only, database migrations, or claims without data provenance. |
Data Analysis
Purpose
Analyze supplied or approved data while preserving provenance, limitations, and uncertainty. The skill prioritizes data authenticity before insight generation.
When to Use
Use for:
- CSV/XLSX/table/JSON metric analysis when the user wants findings or interpretation
- experiment result analysis, cohort/segment comparison, anomaly checks, and trend explanations
- deciding what summaries or charts would best answer a question
Do not use for:
- editing spreadsheet internals or preserving XLSX formatting; use
minimax-xlsx
- chart-only critique or visualization design; use
chart-visualization
- changing production databases or schemas
- making claims when no data source is available
Workflow
- Identify the analysis question, source files, owner, collection window, units, and expected grain.
- Profile the data before interpreting: row/column counts, schema, missing values, duplicates, outliers, joins, and obvious type issues.
- Record cleaning choices. Do not silently drop rows, coerce values, or fill gaps without saying why.
- Analyze with the simplest sufficient method: descriptive stats before models, segments before aggregate-only claims, confidence intervals when relevant.
- Check whether the result answers the user's question or exposes a better question.
- Report findings with caveats, source provenance, and recommended next checks.
Data Authenticity Rules
- Do not invent rows, columns, labels, timestamps, units, denominators, or missing metadata.
- Distinguish observed data, cleaned/derived data, user-provided assumptions, and inference.
- If data is sampled, stale, filtered, synthetic, or incomplete, mark the scope limitation.
- Avoid causal language unless the design supports causality.
Output Contract
STATUS: ANALYZED | PARTIAL | BLOCKED
CONFIDENCE: high | medium | low
QUESTION:
- <analysis question>
DATA PROVENANCE:
- Source:
- Rows/columns or scope:
- Time window / grain / units:
QUALITY CHECKS:
- Missingness:
- Duplicates/outliers:
- Cleaning choices:
FINDINGS:
- <finding> - Evidence: <calculation/source field>
LIMITATIONS:
- <data or method limitation>
NEXT CHECKS:
- <follow-up analysis, validation, or chart>
Provenance
Clean-room AILI/OpenCode adaptation inspired by the public DeerFlow data-analysis skill pattern. No upstream skill text, runtime paths, tools, generated assets, provider assumptions, or external analysis services are copied. Source family: bytedance/deer-flow, MIT License.
